| Title: |
Improving Laplacian Pyramids Regression with Localization in Frequency and Time |
| Authors: |
Hen, Ben; Rabin, Neta; Fernández Pascual, Ángela |
| Contributors: |
Departamento de Ingeniería Informática; Escuela Politécnica Superior |
| Publication Year: |
2024 |
| Collection: |
Universidad Autónoma de Madrid (UAM): Biblos-e Archivo |
| Subject Terms: |
Informática |
| Description: |
Auto-Adaptive Laplacian Pyramids (ALP) is an iterative kernel-based regression model. It constructs a multi-scale representation of the train data, where the multi-scale modes are average residuals. In this work, we propose two extensions of the model. The first is a hybrid approach that combines ALP with Empirical Mode Decomposition to provide localization in the frequency domain. The second modifies ALP to fit datasets with non-uniform noise, which is achieved by computing the optimal stopping criterion in a point-dependent manner. Experimental results demonstrate these models for solar energy prediction and for forecasting epidemiology infections. ; This research was supported by the Israel Science Foundation [Grant 1144/20] |
| Document Type: |
conference object |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
ESANN; October 5-7,2022; Bruges (Belgium); European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; https://hdl.handle.net/10486/711558; 363; 368 |
| Availability: |
https://hdl.handle.net/10486/711558 |
| Rights: |
open access |
| Accession Number: |
edsbas.ECC71F00 |
| Database: |
BASE |